{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__doc__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'copysign', 'cos', 'cosh', 'degrees', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'pi', 'pow', 'radians', 'remainder', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau', 'trunc']\n"
     ]
    }
   ],
   "source": [
    "print(dir(math))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.pi  #3.141592653589793"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148.4131591025766"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.e  #2.718281828459045\n",
    "math.exp(5)\n",
    "#math.pow(math.e,5)  #148.41315910257657"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 x 1 = 1   \n",
      "1 x 2 = 2   2 x 2 = 4   \n",
      "1 x 3 = 3   2 x 3 = 6   3 x 3 = 9   \n",
      "1 x 4 = 4   2 x 4 = 8   3 x 4 = 12  4 x 4 = 16  \n",
      "1 x 5 = 5   2 x 5 = 10  3 x 5 = 15  4 x 5 = 20  5 x 5 = 25  \n",
      "1 x 6 = 6   2 x 6 = 12  3 x 6 = 18  4 x 6 = 24  5 x 6 = 30  6 x 6 = 36  \n",
      "1 x 7 = 7   2 x 7 = 14  3 x 7 = 21  4 x 7 = 28  5 x 7 = 35  6 x 7 = 42  7 x 7 = 49  \n",
      "1 x 8 = 8   2 x 8 = 16  3 x 8 = 24  4 x 8 = 32  5 x 8 = 40  6 x 8 = 48  7 x 8 = 56  8 x 8 = 64  \n",
      "1 x 9 = 9   2 x 9 = 18  3 x 9 = 27  4 x 9 = 36  5 x 9 = 45  6 x 9 = 54  7 x 9 = 63  8 x 9 = 72  9 x 9 = 81  \n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10):\n",
    "    for j in range(1,i+1):\n",
    "        print(f'{j} x {i} = '+str(i * j).center(2),end='  ')\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = -1\n",
    "abs(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uZWbWHyD8vDvcXgy0/vulANjV/vJERLpe+cE6nlxWzKcnFZDXPT3octqlveE+H5gVfj0LeKZV+0wzSzezYcAIYEnHShQR6VoPvrmVhqYQX/rosKBLabdjDiSZ2WPAeUCumRUDtwM/BuaZ2fXAduAKAHdfbWbzgDVAI/Bld2+KUO0iIp3uUH0jD721jYtG5zM8LzvoctrtmOHu7lcfZdXUo2x/J3BnR4oSEQnKo4u3s/9QA7OnnBR0KR0SWxM3RUQiqLahiT+8tpmzT+rDaUN6HXuHKKZwFxEJm1e0g/KDddx0/oigS+kwhbuICFDfGOL3izZROKQXZw7vHXQ5HaZwFxEBnlpWzK7KWm6aOgKztr6yE1sU7iKS8BqbQty1aBPjC3owZURu0OV0CoW7iCS8+e/sYvveQ3zl/PjotYPCXUQSXFPI+c0rGzm1fw4XnNr32DvECIW7iCS0Z1bsZHN5NTedf3Lc9NpB4S4iCayhKcQv/rqBMQNymDamX9DldCqFu4gkrHlFO9i+9xC3XDSKpKT46bWDwl1EElRtQxO/XriRSYN7ct6o+LvsuMJdRBLSw29to/RALbdcPCquxtpbKNxFJOFU1zXyu0WbOOfkPpx9UnzMaz+Swl1EEs4Df99KRXU9t1w0KuhSIkbhLiIJZV91PX94dRNTT+nLxMGxfeXHD6NwF5GE8uuXN1JV18g3p50SdCkRpXAXkYSxraKah97aypWFgxjVr3vQ5USUwl1EEsZPXlhPSlISN184MuhSIq5D4W5m/2Zmq81slZk9ZmYZZtbbzBaY2Ybwc/wOaolIzFi6bR/PrSxh9pTh9M3JCLqciGt3uJvZQOCrQKG7jwWSgZnAbcBCdx8BLAwvi4gExt35r+fXktc9ndlThgddTpfo6LBMCpBpZilAN2AXMB2YE14/B5jRwWOIiHTIC6tKWbptHzdfOJKs9JSgy+kS7Q53d98J/BTYDpQAle7+EpDv7iXhbUqANq+haWazzazIzIrKy8vbW4aIyIeqbWjixy+sY0TfbK44rSDocrpMR4ZletHcSx8GDACyzOy6493f3e9290J3L8zLi7/rOohIdLjv9S1sqzjEdy8dTUpy4swh6cgnvQDY4u7l7t4APAWcDZSZWX+A8PPujpcpInLidu2v4Tcvb+TiMflMGZlYnciOhPt24Ewz62bNV92ZCqwF5gOzwtvMAp7pWIkiIu3zX8+vJeTOdy4ZHXQpXa7dZxbcfbGZPQEsAxqB5cDdQDYwz8yup/kXwBWdUaiIyIl4a3MFz75bwtemjmBQ725Bl9PlOnTa2N1vB24/ormO5l68iEggGptCfH/+agb2zORfzzsp6HICkThnF0QkYTyyeDvrSg/y3UtPJSM1OehyAqFwF5G4svtALT99aT3nnpzLxXF2X9QToXAXkbhyx5/XUNcY4gczxsblHZaOl8JdROLGy+vKeG5lCV89/2SG5WYFXU6gFO4iEheq6xr57tOrGZmfzewpiXkStbXEuMiCiMS9ny94j537a3jihrNIS1G/VT8BEYl5q3ZWcv8bW7jmjMEUDu0ddDlRQeEuIjGtoSnErU++S5/sdG6N81vnnQgNy4hITLvrlU2s3nWA3193Gj0yU4MuJ2qo5y4iMWv1rkp+/fIGpk8YwLSxiTunvS0KdxGJSfWNIb4x7x16ZaXx/cvGBF1O1NGwjIjEpN+8vIF1pQe553OF9MpKC7qcqKOeu4jEnHeL9/PbRZv41KSBXDg6P+hyopLCXURiSk19EzfPe4fc7DRuv1TDMUejYRkRiSl3Pr+GjbureOj6yfToptkxR6Oeu4jEjJdWl/LwW9uZPWU4Hx2RWLfNO1EKdxGJCWUHarn1yXcZOzCHWy4aFXQ5UU/hLiJRLxRybp63gtqGEL+cOVHXjjkOHfoJmVlPM3vCzNaZ2VozO8vMepvZAjPbEH7u1VnFikhiuudvm3ljYwW3Xzaak/Kygy4nJnT0198vgRfc/RRgPLAWuA1Y6O4jgIXhZRGRdinaupf/eXE9Hx/bj6tOHxR0OTGj3eFuZjnAFOA+AHevd/f9wHRgTnizOcCMjhYpIolpT1UdX350GQW9Mvnvz4xL6DsrnaiO9NyHA+XAH81suZnda2ZZQL67lwCEn/u2tbOZzTazIjMrKi8v70AZIhKPmkLOVx9bzv5DDdx17WnkZGja44noSLinAJOA37n7RKCaExiCcfe73b3Q3Qvz8jSlSUTe7+cL3uPvmyr4wYyxjB6QE3Q5Macj4V4MFLv74vDyEzSHfZmZ9QcIP+/uWIkikmheXlfGb17ZyFWFg7iyUOPs7dHucHf3UmCHmbVMOJ0KrAHmA7PCbbOAZzpUoYgklK17qvm3ue8wun8Od0zX5QXaq6OXH7gJeMTM0oDNwOdp/oUxz8yuB7YDV3TwGCKSIA7UNvDFB4tIMvj9daeRkZocdEkxq0Ph7u4rgMI2Vk3tyPuKSOJpCjlfe2w5W/dU89D1ZzC4T7egS4ppunCYiESFn7y4jlfWl/PDGWM566Q+QZcT8/QdXhEJ3FPLivnDq5u57szBXHfmkKDLiQsKdxEJVNHWvdz21ErOHN6b23W7vE6jcBeRwGwqr+KLDxZR0DOT3117GqnJiqTOop+kiASi/GAd//zHJaQkGQ98frLug9rJdEJVRLrcofpGrp/zNnsO1vP47DM1MyYC1HMXkS7V2BTiK48uZ9XOSn5zzUTGD+oZdElxST13EekyoZBz65MreXndbu785FimnpofdElxSz13EekS7s4df17Nk8uKufnCkVx7hqY8RpLCXUS6xE9fWs+cN7cxe8pwbjr/5KDLiXsKdxGJuLsWbeS3r2zi6smD+dbHT9FNN7qAwl1EImrO37fykxfWM33CAH44Y6yCvYvohKqIRMwf39jCHX9ew4Wj8/npFeNJTlKwdxWFu4hExL1/28wPn1vLxWPy+fXVk/Tt0y6mcBeRTveHVzfxo7+s4xMf6ccvZ05UsAdA4S4inequRRv5yQvruXRcf35+1QQFe0AU7iLSKdyd/35hPb9/dRPTJwzgf68YT4qCPTAd/smbWbKZLTezZ8PLvc1sgZltCD/36niZIhLNGptC3Prku/z+1U1ce8ZgfnblBAV7wDrjp/81YG2r5duAhe4+AlgYXhaROFXb0MSNjyxjXlExX506gh/OGKtZMVGgQ+FuZgXAJcC9rZqnA3PCr+cAMzpyDBGJXgdqG5h1/xJeWlPG9y8bzc0XjtQ89ijR0TH3XwDfBLq3ast39xIAdy8xs74dPIaIRKHifYe4/oEiNpVX8cuZE5g+YWDQJUkr7e65m9mlwG53X9rO/WebWZGZFZWXl7e3DBEJwPLt+5jx2zfYVVnDnC9MVrBHoY4My5wDXG5mW4HHgfPN7GGgzMz6A4Sfd7e1s7vf7e6F7l6Yl5fXgTJEpCs9924JM+9+i25pKfzpxrM55+TcoEuSNrQ73N39W+5e4O5DgZnAy+5+HTAfmBXebBbwTIerFJHAuTu/fWUjX350GWMH9uBPN57NyX27H3tHCUQk5rn/GJhnZtcD24ErInAMEelCVXWNfPOJd3h+ZSmXjx/ATz4zjozU5KDLkg/RKeHu7ouAReHXFcDUznhfEQnepvIqbnhoKZvKq/j2J07hSx8drhkxMUDfUBWRo3ppdSnfmPcOqSlJPHz9GZyt8fWYoXAXkQ9obArx87++x29f2cS4gh787rrTGNgzM+iy5AQo3EXkfXbsPcTXHl/Osu37mXn6IL5/+RiNr8cghbuIHPb8yhJuffJdcPjV1RO5fPyAoEuSdlK4iwg19U3857NreGzJdsYP6smvZ05kcJ9uQZclHaBwF0lwy7bv45Z577B5TzU3fOwkvnHRSF2DPQ4o3EUSVG1DEz//63vc89pm+vfI5JEvnqFvm8YRhbtIAnq3eD/fmPcOG3ZXcfXkQXz7E6fSPSM16LKkEyncRRLIofpGfrlwA/f+bQt52enM+cJkPjZS13aKRwp3kQTx8royvvv0anbur+HKwgL+45LR9MhUbz1eKdxF4lxpZS13/Hk1f1lVysl9s5k7+0zOGN4n6LIkwhTuInGqvjHEg29u5Rd/3UBDU4h/v3gUX/rocNJSNBMmESjcReKMu7Nw7W7ufH4tW/ZU87GRefzn9DEM6ZMVdGnShRTuInFkXekBfvDsGt7YWMFJeVn88fOn80+jdKfLRKRwF4kDJZU1/GrhBua+vYOczFTuuHwM15wxWF9GSmAKd5EYVlFVx+8WbeLBt7bh7nzurKF8/YIR9OyWFnRpEjCFu0gMOlDbwL2vbea+17dQ09DEpycV8NWpIxjUW9eDkWYKd5EYsv9QPXP+vo3739hCZU0Dl4zrz79dMJKT+2YHXZpEmXaHu5kNAh4E+gEh4G53/6WZ9QbmAkOBrcCV7r6v46WKJK7dB2q57/UtPPzWNqrrm7jg1Hy+fsEIxg7sEXRpEqU60nNvBL7h7svMrDuw1MwWAP8MLHT3H5vZbcBtwK0dL1Uk8ezYe4g/vLaJeUXFNDaFuGz8AP71vJM4pV9O0KVJlGt3uLt7CVASfn3QzNYCA4HpwHnhzebQfONshbvIcXJ3Fm/Zy/2vb+Gva8tISUri06cVcMPHhmuuuhy3ThlzN7OhwERgMZAfDn7cvcTM2pxka2azgdkAgwcP7owyRGJabUMT89/ZxR/f2MrakgP06pbKDR87ic+dNZR+PTKCLk9iTIfD3cyygSeBr7v7ATM7rv3c/W7gboDCwkLvaB0isWp7xSEef3s7c9/eQUV1PSPzs/nxpz7CjIkDde9SabcOhbuZpdIc7I+4+1Ph5jIz6x/utfcHdne0SJF4U9fYxEury3j87e28sbGCJIPzT+nL588Zxtkn9eF4O0kiR9OR2TIG3AesdfeftVo1H5gF/Dj8/EyHKhSJI++VHWTe2zt4avlO9lbXM7BnJjdfOJIrCgvo3yMz6PIkjnSk534O8FlgpZmtCLd9m+ZQn2dm1wPbgSs6VqJIbCuprGH+il08vWIXa0sOkJJkXDg6n5mTB3PuybkkJ6mXLp2vI7NlXgeO9n/l1Pa+r0g8qKxp4IVVJTy9fBdvbanAHcYP6sntl43m0nEDyOueHnSJEuf0DVWRTlJRVceCNWW8uLqUNzZWUN8UYlhuFl+bOoLpEwYyLFfTGKXrKNxFOmDX/hpeXF3KC6tKeXvrXkIOg3pn8rmzhnDZ+AGMK+ihk6MSCIW7yAlobAqxYsd+Fq0vZ9F7u1m18wAAo/K785XzR3DxmHxG989RoEvgFO4ix7D7YC2vri9n0XvlvL5hD5U1DSQnGZMG9+TWaacwbWw/DblI1FG4ixyhsqaBt7fs5c3NFby5qYI1Jc29877d07lodD7njerLuSNy6ZGZGnClIkencJeEd7C2gaKt+w6H+epdlYQc0lKSOG1wL/794lGcNypPwy0SUxTuklDcne17D7Fs+z6WbdvPsu37WFd6kKaQk5acxITBPbnp/BGcObwPEwf31Nf/JWYp3CWuVdU1snpnJcu2Nwf58u372FNVD0BWWjLjB/XkxvNO4szhfZg0uBeZaQpziQ8Kd4kblYcaWL2rklW7Klm18wCrdlWyZU81Hr4s3bDcLKaMzGPS4F5MGtyLUf2669uhErcU7hJzGptCbNt7iA1lB3mvrIo1u5qDvHhfzeFtBvbMZMyAHGZMGMjYgTlMGNSL3lm6abQkDoW7RK2mkLNj7yHeKzvIht1VvFd2kPWlB9m8p5r6xtDh7Yb26caEQT257swhjB3QgzEDcuilIJcEp3CXQIVCTumBWrbuqWZrxSG2VlSzZU81W/dUs23vofeF+MCemYzMz+ZjI/MYkd+dkfnZnNw3m25p+t9Y5Ej6VyERd7C2gZ37a9i5r4bifTXs3F/Dtopqtu45xLa91dQ2/CPA01KSGNK7G0Nzs/inU/pycl42I/KzGZHfnex0/e8qcrz0r0U6pKEpxJ6qOsoO1FGyvzm4WwK8OcwPcaC28X37pCUnMah3JsNys/joiFyG5mYxLDeLoblZ9M/JIEknOUU6TOEubWpoCrG3up7dB+ooO1BL2cFayg7UsftAbfPygTp2H6ylorr+8GyUFo2Gi64AAAWuSURBVFlpyQzslcnAnpmcNqTX4dcDe2VS0CuT3Kx0BbhIhCncE0R9Y3NYV1TXsbe6vvl11T+WK6rqD7fvqar7QG8bwAz6ZKWTn5NOvx4ZjB/Ug77dM8jPySA/J538nAwKemXSIzNV3+QUCZjCPUa4O3WNIQ7UNFBZ08CB2ubnypoGKg81UFnTeHi5ZV3LtpU1DRyqb2rzfZMMemel0TsrjT5Z6Zw6IIc+LcvZ6eR3T6dvOLxzs9NJTU7q4k8uIu2hcI+QhqYQNQ1N1NY3UdvQ/Lq6vpHquuZHVV1T+PmDbdX1ze1VtY3/2Ka+iaaQf+gxs9KS6ZGZSk74Mah3N8ZmptIj/GgO8ObQbnndIzNVQyQicShi4W5m04BfAsnAve7+40gdqy3uTkOTU98Uor6x1aOpibrGUPO6Vm31jSHqDi83Pzc0hQ4Hc019E3WNzc81DU3UNISobWiiNryutrGJmvrmtpqGYwdxa8lJRlZaMtnpKWSFH9npKeR3zwi/Tj7cnpORQk6rwG555GSmqlctIodFJNzNLBn4LXAhUAy8bWbz3X1NZx5nbckBbnpseauQfv9zZ0lNNjJSk8lMTf7Hc1oyGSlJ9M5KI7Nnc1t6eF1mWhIZKclkpjVv37JPt7SWkP5HkGenp5CekqQxahHpVJHquU8GNrr7ZgAzexyYDnRquHdLS2ZkfjZpyUmkpSSRGn5OS0kivdXr5vXJ4W2M9MPtyUdsk0T6Ee+TkZJEinrEIhJjIhXuA4EdrZaLgTNab2Bms4HZAIMHD27XQYb0yeKua09rZ4kiIvErUl3StsYY3jcI7e53u3uhuxfm5eVFqAwRkcQUqXAvBga1Wi4AdkXoWCIicoRIhfvbwAgzG2ZmacBMYH6EjiUiIkeIyJi7uzea2VeAF2meCnm/u6+OxLFEROSDIjbP3d2fB56P1PuLiMjRaY6fiEgcUriLiMQhhbuISBwyP/Ji3EEUYVYObOvAW+QCezqpnFiQaJ8X9JkThT7ziRni7m1+USgqwr2jzKzI3QuDrqOrJNrnBX3mRKHP3Hk0LCMiEocU7iIicShewv3uoAvoYon2eUGfOVHoM3eSuBhzFxGR94uXnruIiLSicBcRiUMxHe5mNs3M1pvZRjO7Leh6Is3M7jez3Wa2KuhauoqZDTKzV8xsrZmtNrOvBV1TpJlZhpktMbN3wp/5jqBr6gpmlmxmy83s2aBr6SpmttXMVprZCjMr6tT3jtUx9/B9Wt+j1X1agas7+z6t0cTMpgBVwIPuPjboerqCmfUH+rv7MjPrDiwFZsT5f2cDsty9ysxSgdeBr7n7WwGXFlFmdjNQCOS4+6VB19MVzGwrUOjunf7FrVjuuR++T6u71wMt92mNW+7+GrA36Dq6kruXuPuy8OuDwFqab+MYt7xZVXgxNfyIzV7YcTKzAuAS4N6ga4kXsRzubd2nNa7/0Sc6MxsKTAQWB1tJ5IWHKFYAu4EF7h7vn/kXwDeBUNCFdDEHXjKzpeH7SneaWA73Y96nVeKHmWUDTwJfd/cDQdcTae7e5O4TaL5F5WQzi9thODO7FNjt7kuDriUA57j7JODjwJfDQ6+dIpbDXfdpTRDhcecngUfc/amg6+lK7r4fWARMC7iUSDoHuDw8/vw4cL6ZPRxsSV3D3XeFn3cDf6J5uLlTxHK46z6tCSB8cvE+YK27/yzoerqCmeWZWc/w60zgAmBdsFVFjrt/y90L3H0ozf+OX3b36wIuK+LMLCs8SQAzywIuAjptJlzMhru7NwIt92ldC8yL9/u0mtljwJvAKDMrNrPrg66pC5wDfJbm3tyK8OMTQRcVYf2BV8zsXZo7MQvcPWGmByaQfOB1M3sHWAI85+4vdNabx+xUSBERObqY7bmLiMjRKdxFROKQwl1EJA4p3EVE4pDCXUQkDincRUTikMJdRCQO/T8BVI6jU/SAGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "plt.title(\"y = exp(x)\")\n",
    "x = np.arange(0,5,0.001)\n",
    "y = [math.exp(i) for i in x]\n",
    "plt.plot(x,y,label=\"exp\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "math?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import math\n",
    "\n",
    "plt.title(\"Mother's Day\")\n",
    "t = np.arange(-3,3,0.01)\n",
    "x = [16 * math.pow(math.sin(i),3) for i in t]\n",
    "y = [13*math.cos(i) - 5*math.cos(2*i)-2*math.cos(3*i)-math.cos(4*i) for i in t]\n",
    "plt.plot(x,y,label=\"heart\",color = \"r\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sin(α) = b / c\n",
    "cos(α) = a / c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sympy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "915"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dir(sympy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "SymPy is a Python library for symbolic mathematics. It aims to become a\n",
      "full-featured computer algebra system (CAS) while keeping the code as simple\n",
      "as possible in order to be comprehensible and easily extensible.  SymPy is\n",
      "written entirely in Python. It depends on mpmath, and other external libraries\n",
      "may be optionally for things like plotting support.\n",
      "\n",
      "See the webpage for more information and documentation:\n",
      "\n",
      "    https://sympy.org\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(sympy.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sympy?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
